TY - GEN
T1 - An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization
AU - Kermany, Naime Ranjbar
AU - Zhao, Weiliang
AU - Yang, Jian
AU - Wu, Jia
AU - Pizzato, Luiz
PY - 2020
Y1 - 2020
N2 - In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.
AB - In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.
KW - Diversity
KW - Long-tail recommendation
KW - Multi-objective evolutionary optimization
KW - Multi-stakeholder recommender systems
KW - P-fairness
UR - http://www.scopus.com/inward/record.url?scp=85099218340&partnerID=8YFLogxK
U2 - 10.1109/SCC49832.2020.00074
DO - 10.1109/SCC49832.2020.00074
M3 - Conference proceeding contribution
AN - SCOPUS:85099218340
T3 - Proceedings of the IEEE International Conference on Services Computing SCC
SP - 478
EP - 480
BT - Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Los Alamitos, Ca
T2 - 13th IEEE International Conference on Services Computing, SCC 2020
Y2 - 18 October 2020 through 24 October 2020
ER -